Tamara Lorenz

Rhythm Patterns Interaction - Synchronization Behavior for Human-Robot Joint Action

Interactive behavior among humans is governed by the dynamics of movement synchronization in a variety of repetitive tasks. This requires the interaction partners to perform for example rhythmic limb swinging or even goal-directed arm movements. Inspired by that essential feature of human interaction, we present a novel concept and design methodology to synthesize goal-directed synchronization behavior for robotic agents in repetitive joint action tasks. The agents’ tasks are described by closed movement trajectories and interpreted as limit cycles, for which instantaneous phase variables are derived based on oscillator theory. Events segmenting the trajectories into multiple primitives are introduced as anchoring points for enhanced synchronization modes. Utilizing both continuous phases and discrete events in a unifying view, we design a continuous dynamical process synchronizing the derived modes. Inverse to the derivation of phases, we also address the generation of goal-directed movements from the behavioral dynamics. The developed concept is implemented to an anthropomorphic robot. For evaluation of the concept an experiment is designed and conducted in which the robot performs a prototypical pick-and-place task jointly with human partners. The effectiveness of the designed behavior is successfully evidenced by objective measures of phase and event synchronization. Feedback gathered from the participants of our exploratory study suggests a subjectively pleasant sense of interaction created by the interactive behavior. The results highlight potential applications of the synchronization concept both in motor coordination among robotic agents and in enhanced social interaction between humanoid agents and humans.

Modeling inter-human movement coordination: synchronization governs joint task dynamics

Human interaction partners tend to synchronize their movements during repetitive actions such as walking. Research of inter-human coordination in purely rhythmic action tasks reveals that the observed patterns of interaction are dominated by synchronization effects. Initiated by our finding that human dyads synchronize their arm movements even in a goal-directed action task, we present a step-wise approach to a model of inter-human movement coordination. In an experiment, the hand trajectories of ten human dyads are recorded. Governed by a dynamical process of phase synchronization, the participants establish in-phase as well as anti-phase relations. The emerging relations are successfully reproduced by the attractor dynamics of coupled phase oscillators inspired by the Kuramoto model. Three different methods on transforming the motion trajectories into instantaneous phases are investigated and their influence on the model fit to the experimental data is evaluated. System identification technique allows us to estimate the model parameters, which are the coupling strength and the frequency detuning among the dyad. The stability properties of the identified model match the relations observed in the experimental data. In short, our model predicts the dynamics of inter-human movement coordination. It can directly be implemented to enrich human–robot interaction.

Synchrony and Reciprocity: Key Mechanisms for Social Companion Robots in Therapy and Care

Studies and concepts for social companion robots in therapy and care exist, however, they often lack the integration of convincing behavioral and social key mechanisms which enable a positive and successfull interaction experience. In this article we argue that synchrony and reciprocity are two key mechanisms of human interaction which affect both in the behavioral level (movements) and in the social level (relationships). Given that both a change in movement behavior and social behavior are an objective in the contexts of aging-in-place, neurocognitive and neurophysical rehabilitation, and depression, these key mechanisms should also be included in the interaction with social companion robots in therapy and care. We give an overview on the two concepts ranging from a social neuroscience over a behavioral towards a sociological perspective and argue that both concepts affect each other and are up to now only marginally applied in human–robot interaction. To support this claim, we provide a survey on existing social companion robots for aging-in-place (pet robots and household robots), neurocognitive impairments (autism and dementia), neurophysical impairments (brain injury, cerebral palsy, and Parkinson’s disease), and depression. We emphasize to what extend synchrony and reciprocity are already included into the respective applications. Finally, based on the survey and the previous argumentation on the importance of synchrony and reciprocity, we provide a discussion about potential future steps for the inclusion of these concepts to social companion robots in therapy and care.

Synchronization in a goal-directed task: Human movement coordination with each other and robotic partners

Synchronization occurs frequently in human behaviour: Everybody has experienced that in a group of people walking pace tends to equalize. The phenomenon of synchrony has been established in the literature in tasks which have little in common with daily life such as pendulum swinging and chair rocking. We extend the knowledge about human movement synchronization by showing that it also occurs during goal-directed actions. In a first experiment, we investigate how synchrony emerges develops over time. In a second experiment, we show that humans also synchronize their actions with a robot. Results are interpreted in the light of joint action theory. Possible implications and improvements for human-robot interaction are discussed.

Movement synchronization fails during non-adaptive human-robot interaction

Interpersonal movement synchronization is a phenomenon that does not only increase the predictability of movements; it also increases rapport among people. In this line, synchronization might enhance human-robot interaction. An experiment is presented which explores to what extend a human synchronizes own movements to a non-adaptive robot during a repetitive tapping task. It is shown that the human does not take over the complete effort of movement adaptation to reach synchronization, which indicates the need for adaptive robots.

Increasing perceived value between human and robots — Measuring legibility in human aware navigation

Robots will more and more enter our daily life. In order to increase their acceptance it is necessary that their movements and behavior are predictable. With our present experiment we assess the acceptance of autonomous robots in human working and living environments. As a specific indicator we define legibility as an important prerequisite for user acceptance. In a simulator study participants rated the navigation behavior of a robot with regard to several aspects of legibility. Results show that Human Aware Navigation is a method to increase the perceived value of robot navigation behavior.

Dyadic movement synchronization while performing incongruent trajectories requires mutual adaptation.

Unintentional movement synchronization is often emerging between interacting humans. In the present study, we investigate the extent to which the incongruence of movement trajectories has an influence on unintentional dyadic movement synchronization. During a target-directed tapping task, a participant repetitively moved between two targets in front of another participant who performed the same task in parallel but independently. When the movement path of one participant was changed by placing an obstacle between the targets, the degree of their unintentional movement synchronization was measured. Movement synchronization was observed despite of their substantially different movement trajectories. A deeper investigation of the participants unintentional behavior shows, that although the actor who cleared the obstacle puts unintentional effort in establishing synchrony by increasing movement velocity—the other actor also unintentionally adjusted his/her behavior by increasing dwell times. Results are discussed in the light of joint action, movement interference and obstacle avoidance behavior.

Disagreement-aware physical assistance through risk-sensitive optimal feedback control

Proactive physical robotic assistance in the presence of human prediction uncertainty is a very challenging control problem. In this paper we propose a risk-sensitive optimal feedback controller for physical assistance that autonomously adapts the robots behavior even during unknown situations. Using a probabilistic model to represent the cooperative task execution behavior and modeling the human as a source of process noise in the system, the proposed assistive controller proactively contributes to the task anticipating the human motion. Estimating online the current level of disagreement and prediction uncertainty, the assistive controller consequently calculates the optimal task contribution providing higher adaptability. A psychological evaluation compares different assistive control strategies in a virtual scenario using a two-Degree-of-Freedom haptic experimental setup. Results show that considering the current level of disagreement enhances the performance of the controller in terms of helpfulness and human effort minimization.

Inferring the goal of an approaching agent: A human-robot study

The ability to infer intentions and predict actions enables coordinating of ones own actions with those of another human and allows smooth and intuitive interaction. The aim to achieve equally effective human-robot interactions is a crucial aspect of current robotic studies. Thus, we assume that studying human-human interaction provides valuable insights allowing to implement mutual intention recognition and action prediction in robotic systems. A common scenario of interaction, be it in everyday life or in an industrial setting, is that two or more agents share the same workspace and perform tasks without interference. If humans are involved, the robots should act sufficiently predictable to enable the human to attribute goals and predict motion trajectories. In the present work, we first analyzed how well a human recognizes the goal of another person entering the room, and whether this ability is deteriorated by concealing gaze direction of the other person. In a second setup, the same experiment was repeated by replacing the approaching person with a wheeled robot. On average, the distance at which subjects predicted the goal of the approaching agent was approx. 4 m and depended on subject and goal position, but not on the type of agent. However, goal attribution showed a considerable proportion of errors for the robot (19%), much less for a human with hidden gaze direction (6%), and almost none for a human with visible gaze (1%). Thus, our subjects apparently decided on the goal of the approaching agent without taking into account the reliability of directional cues, thus resulting in more errors. In a human-robot setting, such wrong predictions about robotic behavior may easily lead to dangerous situations. For smooth and safe interaction, it is therefore important to ameliorate the predictability of robotic actions.

Robotic Billiards: Understanding Humans in Order to Counter Them

Ongoing technological advances in the areas of computation, sensing, and mechatronics enable robotic-based systems to interact with humans in the real world. To succeed against a human in a competitive scenario, a robot must anticipate the human behavior and include it in its own planning framework. Then it can predict the next human move and counter it accordingly, thus not only achieving overall better performance but also systematically exploiting the opponents weak spots. Pool is used as a representative scenario to derive a model-based planning and control framework where not only the physics of the environment but also a model of the opponent is considered. By representing the game of pool as a Markov decision process and incorporating a model of the human decision-making based on studies, an optimized policy is derived. This enables the robot to include the opponents typical game style into its tactical considerations when planning a stroke. The results are validated in simulations and real-life experiments with an anthropomorphic robot playing pool against a human.